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Proceedings Paper

Kernel-based reclassification algorithm applied on very high spatial resolution satellite imagery of complex ecosystems
Author(s): Iphigenia Keramitsoglou; Charalambos Kontoes; Panagiotis Elias; Nicolaos Sifakis; Eleni Fitoka; Stefan Weiers
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Paper Abstract

Kernel-based reclassification algorithm derives information on specific thematic classes on the basis of the frequency and spatial arrangement of land cover classes within a square kernel. This algorithm has been originally developed and validated for the urban environment. The present work investigates the potential of projecting this technique to the classification of very high spatial resolution satellite imagery of natural ecosystems. For that purpose a software tool has been developed. The output, apart from the reclassified image, includes a post-classification probability map which shows the areas where the kernel reclassification algorithm has given valid results. The software was tested on an IKONOS image of Lake Kerkini (Greece), a wetland of great ecological value, included in the NATURA 2000 list of ecosystems. The results show that the algorithm has responded successfully in most cases overcoming problems previously encountered by pixel-based classifiers, such as pixel noise.

Paper Details

Date Published: 24 February 2004
PDF: 8 pages
Proc. SPIE 5232, Remote Sensing for Agriculture, Ecosystems, and Hydrology V, (24 February 2004); doi: 10.1117/12.511071
Show Author Affiliations
Iphigenia Keramitsoglou, National Observatory of Athens (Greece)
Charalambos Kontoes, National Observatory of Athens (Greece)
Panagiotis Elias, National Observatory of Athens (Greece)
Nicolaos Sifakis, National Observatory of Athens (Greece)
Eleni Fitoka, Greek Biotope/Wetland Ctr. (Greece)
Stefan Weiers, DLR (Germany)

Published in SPIE Proceedings Vol. 5232:
Remote Sensing for Agriculture, Ecosystems, and Hydrology V
Manfred Owe; Guido D'Urso; Jose F. Moreno; Alfonso Calera, Editor(s)

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